Exploratory Analysis for Occipital Lobe

#setwd('/afs/inf.ed.ac.uk/user/s17/s1725186/Documents/PhD-Models/FirstPUModel/RMarkdowns')

library(tidyverse) ; library(reshape2) ; library(glue) ; library(plotly) ; library(plotlyutils)
library(RColorBrewer) ; library(viridis) ; require(gridExtra) ; library(GGally)
library(Rtsne)
library(ClusterR)
library(DESeq2) ; library(biomaRt)
library(knitr)

Load preprocessed dataset (preprocessing code in 20_02_25_data_preprocessing.Rmd)

# Gandal dataset
load('./../Data/preprocessed_data.RData')
datExpr = datExpr %>% data.frame
DE_info = DE_info %>% data.frame

# GO Neuronal annotations: regex 'neuron' in GO functional annotations and label the genes that make a match as neuronal
GO_annotations = read.csv('./../Data/genes_GO_annotations.csv')
GO_neuronal = GO_annotations %>% filter(grepl('neuron', go_term)) %>% 
              mutate('ID'=as.character(ensembl_gene_id)) %>% 
              dplyr::select(-ensembl_gene_id) %>% distinct(ID) %>%
              mutate('Neuronal'=1)

# Update DE_info with Neuronal information
DE_info = DE_info %>% mutate('ID'=rownames(.)) %>% left_join(GO_neuronal, by='ID') %>%
  mutate(Neuronal=ifelse(is.na(Neuronal), 0, Neuronal)) %>%
  mutate(significant=padj<0.05 & !is.na(padj))

rm(GO_annotations)

All samples together

• There seem to be two different behaviours in mean expression by gene

plot_data = data.frame('ID'=rownames(datExpr), 'Mean'=rowMeans(datExpr))
p1 = ggplotly(plot_data %>% ggplot(aes(Mean)) + geom_density(color='#0099cc', fill='#0099cc', alpha=0.3) +
              scale_x_log10() + theme_minimal())

plot_data = data.frame('ID'=colnames(datExpr), 'Mean'=colMeans(datExpr))
p2 = ggplotly(plot_data %>% ggplot(aes(Mean)) + geom_density(color='#0099cc', fill='#0099cc', alpha=0.3) +
              theme_minimal() + ggtitle('Mean expression density by Gene (left) and by Sample (right)'))

subplot(p1, p2, nrows=1)

rm(p1, p2, plot_data)

Grouping genes by Neuronal tag and samples by Diagnosis

• The two groups of genes seem to be partially characterised by genes with Neuronal function

• In general, the autism group has a bigger mean but this time the control group seems to have a wider variance

plot_data = data.frame('ID'=rownames(datExpr), 'Mean'=rowMeans(datExpr)) %>% 
            left_join(GO_neuronal, by='ID') %>% mutate('Neuronal'=ifelse(is.na(Neuronal),F,T))
p1 = plot_data %>% ggplot(aes(Mean, color=Neuronal, fill=Neuronal)) + geom_density(alpha=0.3) +
                   scale_x_log10() + theme_minimal() + theme(legend.position='bottom') + 
                   ggtitle('Mean expression density by gene')

plot_data = data.frame('ID'=colnames(datExpr), 'Mean'=colMeans(datExpr)) %>% 
            mutate('ID'=substring(ID,2)) %>% left_join(datMeta, by=c('ID'='Dissected_Sample_ID'))
p2 = plot_data %>% ggplot(aes(Mean, color=Diagnosis, fill=Diagnosis)) + geom_density(alpha=0.3) +
                   theme_minimal() + theme(legend.position='bottom') + 
                   ggtitle('Mean expression density by Sample')

grid.arrange(p1, p2, nrow=1)

rm(GO_annotations, plot_data, p1, p2)

ASD vs CTL

In general there doesn’t seem to be a lot of variance in mean expression between autism and control samples by gene.

plot_data = data.frame('ID'=rownames(datExpr),
                       'ASD'=rowMeans(datExpr[,datMeta$Diagnosis=='ASD']),
                       'CTL'=rowMeans(datExpr[,datMeta$Diagnosis!='ASD']))

plot_data %>% ggplot(aes(ASD,CTL)) + geom_point(alpha=0.1, color='#0099cc') + 
         geom_abline(color='gray') + ggtitle('Mean expression ASD vs CTL') + theme_minimal()

Grouping genes and samples by Diagnosis

• There doesn’t seem to be a noticeable difference between mean expression by gene between Diagnosis groups

• Samples with autism tend to have a narrower dispersion of mean expression with higher values than the control group (as we had already seen above)

plot_data = rbind(data.frame('Mean'=rowMeans(datExpr[,datMeta$Diagnosis=='ASD']), 'Diagnosis'='ASD'),
                  data.frame('Mean'=rowMeans(datExpr[,datMeta$Diagnosis!='ASD']), 'Diagnosis'='CTL')) %>%
            mutate('Diagnosis'=factor(Diagnosis, levels=c('CTL','ASD')))
p1 = ggplotly(plot_data %>% ggplot(aes(Mean, color=Diagnosis, fill=Diagnosis)) + 
              geom_density(alpha=0.3) + scale_x_log10() + theme_minimal())

plot_data = rbind(data.frame('Mean'=colMeans(datExpr[,datMeta$Diagnosis=='ASD']), 'Diagnosis'='ASD'),
                  data.frame('Mean'=colMeans(datExpr[,datMeta$Diagnosis!='ASD']), 'Diagnosis'='CTL')) %>%
            mutate('Diagnosis'=factor(Diagnosis, levels=c('CTL','ASD')))
p2 = ggplotly(plot_data %>% ggplot(aes(Mean, color=Diagnosis, fill=Diagnosis)) + 
              geom_density(alpha=0.3) + theme_minimal() +
              ggtitle('Mean expression by Gene (left) and by Sample (right) grouped by Diagnosis'))

subplot(p1, p2, nrows=1)

rm(p1, p2, plot_data)

---

Visualisations

Samples

PCA

The first principal separates perfectly the two diagnosis

pca = datExpr %>% t %>% prcomp

plot_data = data.frame('ID'=colnames(datExpr), 'PC1' = pca$x[,1], 'PC2' = pca$x[,2]) %>% 
            mutate('ID'=substring(ID,2)) %>% left_join(datMeta, by=c('ID'='Dissected_Sample_ID')) %>% 
            dplyr::select('ID','PC1','PC2','Diagnosis') %>% 
            mutate('Diagnosis'=factor(Diagnosis, levels=c('CTL','ASD')))

plot_data %>% ggplot(aes(PC1, PC2, color=Diagnosis)) + geom_point() + theme_minimal() + ggtitle('PCA') +
              xlab(paste0('PC1 (',round(100*summary(pca)$importance[2,1],1),'%)')) +
              ylab(paste0('PC2 (',round(100*summary(pca)$importance[2,2],1),'%)'))

rm(pca, plot_data)

MDS

Looks exactly the same as the PCA visualisation, just inverting the x axis

d = datExpr %>% t %>% dist
fit = cmdscale(d, k=2)

plot_data = data.frame('ID'=colnames(datExpr), 'C1'=fit[,1], 'C2'=fit[,2]) %>%
            mutate('ID'=substring(ID,2)) %>% left_join(datMeta, by=c('ID'='Dissected_Sample_ID')) %>% 
            dplyr::select('C1','C2','Diagnosis') %>%
            mutate('Diagnosis'=factor(Diagnosis, levels=c('CTL','ASD')))

plot_data %>% ggplot(aes(C1, C2, color=Diagnosis)) + geom_point() + theme_minimal() + ggtitle('MDS')

rm(d, fit, plot_data)

t-SNE

Higher perplexities seem to capture the difference between diagnosis better. You can separate the diagonsis perfectly with a line using the highest perplexity

#perplexities = c(2,5,10,25)
perplexities = c(1,2,3,4)
ps = list()

for(i in 1:length(perplexities)){
  set.seed(123)
  tsne = datExpr %>% t %>% Rtsne(perplexity=perplexities[i])
  plot_data = data.frame('ID'=colnames(datExpr), 'C1'=tsne$Y[,1], 'C2'=tsne$Y[,2]) %>%
              mutate('ID'=substring(ID,2)) %>% left_join(datMeta, by=c('ID'='Dissected_Sample_ID')) %>%
              dplyr::select('C1','C2','Diagnosis','Subject_ID') %>%
              mutate('Diagnosis'=factor(Diagnosis, levels=c('CTL','ASD')))
  ps[[i]] = plot_data %>% ggplot(aes(C1, C2, color=Diagnosis)) + geom_point() + theme_minimal() +
            ggtitle(paste0('Perplexity=',perplexities[i])) + theme(legend.position='none')
}

grid.arrange(grobs=ps, nrow=2)

rm(ps, perplexities, tsne, i)

Genes

PCA

• First Principal Component explains over 99% of the total variance

• There’s a really strong correlation between the mean expression of a gene and the 1st principal component

pca = datExpr %>% prcomp

plot_data = data.frame( 'PC1' = pca$x[,1], 'PC2' = pca$x[,2], 'MeanExpr'=rowMeans(datExpr))

plot_data %>% ggplot(aes(PC1, PC2, color=MeanExpr)) + geom_point(alpha=0.3) + theme_minimal() + 
              scale_color_viridis() + ggtitle('PCA') +
              xlab(paste0('PC1 (',round(100*summary(pca)$importance[2,1],1),'%)')) +
              ylab(paste0('PC2 (',round(100*summary(pca)$importance[2,2],1),'%)'))

rm(pca, plot_data)

t-SNE

Higher perplexities capture a cleaner visualisation of the data ordered by mean expression, in a similar (although not as linear) way to PCA

perplexities = c(1,2,5,10,50,100)
ps = list()

for(i in 1:length(perplexities)){
  tsne = read.csv(paste0('./../Visualisations/tsne_perplexity_',perplexities[i],'.csv'))
  plot_data = data.frame('C1'=tsne[,1], 'C2'=tsne[,2], 'MeanExpr'=rowMeans(datExpr))
  ps[[i]] = plot_data %>% ggplot(aes(C1, C2, color=MeanExpr)) + geom_point(alpha=0.5) + theme_minimal() +
            scale_color_viridis() + ggtitle(paste0('Perplexity = ',perplexities[i])) + theme(legend.position='none')
}

grid.arrange(grobs=ps, nrow=2)

rm(perplexities, ps, tsne, i)

Differential Expression Analysis

• 1222 genes (~7%) are significant using a threshold of 0.05 for the adjusted p-value and a without a log Fold Change threshold (keeping the null hypothesis \(H_0: lfc=0\))

cat(paste0(sum(is.na(DE_info$padj)), ' (~', round(100*(sum(is.na(DE_info$padj))/nrow(DE_info))),
           '%) of the p-values couldn\'t be calculated'))

## 960 (~6%) of the p-values couldn't be calculated

table(DE_info$padj<0.05, useNA='ifany')

## 
## FALSE  TRUE  <NA> 
## 14309  1222   960

p = DE_info %>% ggplot(aes(log2FoldChange, padj, color=significant)) + geom_point(alpha=0.2) + 
    scale_y_sqrt() + xlab('log2 Fold Change') + ylab('Adjusted p-value') + theme_minimal()
ggExtra::ggMarginal(p, type = 'density', color='gray', fill='gray', size=10)

rm(p)

• There is a clear negative relation between lfc and mean expression, for both differentially expressed and not differentially expressed groups of genes

• The relation is strongest for genes with low levels of expression

• The p-value of the genes with the lowest levels of expression couldn’t be calculated. DESeq2 does this with genes where it considers the level of expression is not high enough to get robust results

plot_data = data.frame('ID'=rownames(datExpr), 'meanExpr'=rowMeans(datExpr)) %>% left_join(DE_info, by='ID') %>%
            mutate('statisticallySignificant' = ifelse(is.na(padj),NA, ifelse(padj<0.05, TRUE, FALSE)),
                   'alpha' = ifelse(padj>0.05 | is.na(padj), 0.1, 0.5))

plot_data %>% ggplot(aes(meanExpr, abs(log2FoldChange), color=statisticallySignificant)) + 
              geom_point(alpha = plot_data$alpha) + geom_smooth(method='lm') + 
              theme_minimal() + scale_y_sqrt() + theme(legend.position = 'bottom') +
              xlab('Mean Expression') + ylab('abs(lfc)') + ggtitle('Log fold change by level of expression')

• When filtering for differential expression, the points separate into two clouds depending on whether they are over or underexpressed

• The top cloud corresponds to the underexpressed genes and the bottom to the overexpressed ones

datExpr_DE = datExpr[DE_info$significant,]

pca = datExpr_DE %>% prcomp

plot_data = cbind(data.frame('PC1'=pca$x[,1], 'PC2'=pca$x[,2]), DE_info[DE_info$significant==TRUE,])

pos_zero = -min(plot_data$log2FoldChange)/(max(plot_data$log2FoldChange)-min(plot_data$log2FoldChange))
p = plot_data %>% ggplot(aes(PC1, PC2, color=log2FoldChange)) + geom_point(alpha=0.5) +
                  scale_color_gradientn(colours=c('#F8766D','#faa49e','white','#00BFC4','#009499'), 
                                        values=c(0, pos_zero-0.05, pos_zero, pos_zero+0.05, 1)) +
                  theme_minimal() + ggtitle('
PCA of differentially expressed genes') + # This is on purpose, PDF doesn't save well without this white space (?)
                  xlab(paste0('PC1 (',round(100*summary(pca)$importance[2,1],1),'%)')) +
                  ylab(paste0('PC2 (',round(100*summary(pca)$importance[2,2],1),'%)')) + theme(legend.position = 'bottom')
ggExtra::ggMarginal(p, type='density', color='gray', fill='gray', size=10)

rm(pos_zero, p)

Separating the genes into these two groups: Salmon: under-expressed, aqua: over-expressed

plot_data = plot_data %>% mutate('Group'=ifelse(log2FoldChange>0,'overexpressed','underexpressed')) %>%
            mutate('Group' = factor(Group, levels=c('underexpressed','overexpressed')))

rm(pca)

List of top DE genes

# Get genes names
getinfo = c('ensembl_gene_id','external_gene_id')
mart = useMart(biomart='ENSEMBL_MART_ENSEMBL', dataset='hsapiens_gene_ensembl', host='feb2014.archive.ensembl.org')
gene_names = getBM(attributes=getinfo, filters=c('ensembl_gene_id'), values=plot_data$ID, mart=mart) %>% 
             rename(external_gene_id = 'gene_name', ensembl_gene_id = 'ID')

## Batch submitting query [============>-------------] 50% eta: 0s

plot_data = plot_data %>% left_join(gene_names, by='ID') %>% arrange(-abs(log2FoldChange)) %>% 
            top_n(50, wt=log2FoldChange)

kable(plot_data %>% dplyr::select(ID, gene_name, log2FoldChange, padj, Neuronal, Group) %>%
      arrange(padj))

ID	gene_name	log2FoldChange	padj	Neuronal	Group
ENSG00000188064	WNT7B	1.624149	0.0000306	1	overexpressed
ENSG00000146250	PRSS35	2.342676	0.0000323	0	overexpressed
ENSG00000158966	CACHD1	1.294324	0.0000655	0	overexpressed
ENSG00000153993	SEMA3D	1.313247	0.0001419	0	overexpressed
ENSG00000139874	SSTR1	1.384457	0.0001816	0	overexpressed
ENSG00000147402	GABRQ	1.789597	0.0004994	0	overexpressed
ENSG00000103528	SYT17	1.309523	0.0006337	0	overexpressed
ENSG00000203685	C1orf95	1.633790	0.0010345	0	overexpressed
ENSG00000169432	SCN9A	1.466569	0.0010762	1	overexpressed
ENSG00000136883	KIF12	1.395533	0.0013199	0	overexpressed
ENSG00000011638	TMEM159	1.421228	0.0014446	0	overexpressed
ENSG00000178531	CTXN1	1.245250	0.0015162	0	overexpressed
ENSG00000204060	FOXO6	1.251835	0.0019657	0	overexpressed
ENSG00000105048	TNNT1	1.313810	0.0032765	0	overexpressed
ENSG00000130055	GDPD2	1.474136	0.0035618	0	overexpressed
ENSG00000091831	ESR1	1.725237	0.0036486	0	overexpressed
ENSG00000175206	NPPA	1.621458	0.0036486	0	overexpressed
ENSG00000183287	CCBE1	1.673617	0.0037503	0	overexpressed
ENSG00000198883	PNMA5	1.991329	0.0040498	0	overexpressed
ENSG00000088882	CPXM1	1.609092	0.0047747	0	overexpressed
ENSG00000109758	HGFAC	1.387566	0.0048854	0	overexpressed
ENSG00000139211	AMIGO2	1.507537	0.0051893	0	overexpressed
ENSG00000101198	NKAIN4	1.289738	0.0051893	0	overexpressed
ENSG00000272636	DOC2B	1.263875	0.0052857	0	overexpressed
ENSG00000180332	KCTD4	1.613111	0.0072762	0	overexpressed
ENSG00000181085	MAPK15	2.141371	0.0086524	0	overexpressed
ENSG00000101327	PDYN	1.279954	0.0087242	0	overexpressed
ENSG00000183729	NPBWR1	1.384447	0.0093729	0	overexpressed
ENSG00000152580	IGSF10	1.273399	0.0115950	0	overexpressed
ENSG00000163520	FBLN2	1.274193	0.0122015	0	overexpressed
ENSG00000187848	P2RX2	3.173073	0.0125413	1	overexpressed
ENSG00000050628	PTGER3	1.682477	0.0130032	0	overexpressed
ENSG00000167754	KLK5	1.625957	0.0151551	0	overexpressed
ENSG00000102854	MSLN	1.764925	0.0156539	0	overexpressed
ENSG00000113494	PRLR	1.285302	0.0160946	0	overexpressed
ENSG00000269430	LRRC3DN	2.300848	0.0163198	0	overexpressed
ENSG00000197901	SLC22A6	1.369033	0.0174635	0	overexpressed
ENSG00000146197	SCUBE3	1.234929	0.0211542	0	overexpressed
ENSG00000205592	MUC19	1.914382	0.0219089	0	overexpressed
ENSG00000101188	NTSR1	1.236371	0.0238657	0	overexpressed
ENSG00000163395	IGFN1	1.510136	0.0244513	0	overexpressed
ENSG00000166869	CHP2	2.303107	0.0275230	0	overexpressed
ENSG00000198840	MT-ND3	1.454115	0.0277443	0	overexpressed
ENSG00000186439	TRDN	1.409698	0.0315766	0	overexpressed
ENSG00000112539	C6orf118	1.771463	0.0322122	0	overexpressed
ENSG00000172554	SNTG2	1.334849	0.0328812	0	overexpressed
ENSG00000105641	SLC5A5	1.636044	0.0354082	0	overexpressed
ENSG00000196932	TMEM26	1.308984	0.0399838	0	overexpressed
ENSG00000117650	NEK2	1.491565	0.0402647	0	overexpressed
ENSG00000143867	OSR1	1.703651	0.0422474	0	overexpressed

Effects of modifying the log fold change threshold

Not only we have very few DE genes, but we lose most of them from very low log Fold Change thresholds

fc_list = seq(1, 1.3, 0.01)
#fc_list = c(seq(1,1.01, 0.002), seq(1.01, 1.04, 0.01))

n_genes = nrow(datExpr)

# Calculate PCAs
datExpr_pca_samps = datExpr %>% data.frame %>% t %>% prcomp(scale.=TRUE)
datExpr_pca_genes = datExpr %>% data.frame %>% prcomp(scale.=TRUE)

# Initialice DF to save PCA outputs
pcas_samps = datExpr_pca_samps$x %>% data.frame %>% dplyr::select(PC1:PC2) %>% 
             mutate('ID'=colnames(datExpr), 'fc'=-1, PC1=scale(PC1), PC2=scale(PC2))
pcas_genes = datExpr_pca_genes$x %>% data.frame %>% dplyr::select(PC1:PC2) %>% 
             mutate('ID'=rownames(datExpr), 'fc'=-1, PC1=scale(PC1), PC2=scale(PC2))

pca_samps_old = pcas_samps
pca_genes_old = pcas_genes

for(fc in fc_list){
  
  # Recalculate DE_info with the new threshold (p-values change) an filter DE genes
  DE_genes = results(dds, lfcThreshold=log2(fc), altHypothesis='greaterAbs') %>% data.frame %>%
             mutate('ID'=rownames(.)) %>% filter(padj<0.05)
  
  datExpr_DE = datExpr %>% data.frame %>% filter(rownames(.) %in% DE_genes$ID)
  n_genes = c(n_genes, nrow(DE_genes))
  
  # Calculate PCAs
  datExpr_pca_samps = datExpr_DE %>% t %>% prcomp(scale.=TRUE)
  datExpr_pca_genes = datExpr_DE %>% prcomp(scale.=TRUE)

  # Create new DF entries
  pca_samps_new = datExpr_pca_samps$x %>% data.frame %>% dplyr::select(PC1:PC2) %>% 
                  mutate('ID'=colnames(datExpr), 'fc'=fc, PC1=scale(PC1), PC2=scale(PC2))
  pca_genes_new = datExpr_pca_genes$x %>% data.frame %>% dplyr::select(PC1:PC2) %>% 
                  mutate('ID'=DE_genes$ID, 'fc'=fc, PC1=scale(PC1), PC2=scale(PC2))  
  
  # Change PC sign if necessary
  if(cor(pca_samps_new$PC1, pca_samps_old$PC1)<0) pca_samps_new$PC1 = -pca_samps_new$PC1
  if(cor(pca_samps_new$PC2, pca_samps_old$PC2)<0) pca_samps_new$PC2 = -pca_samps_new$PC2
  if(cor(pca_genes_new[pca_genes_new$ID %in% pca_genes_old$ID,]$PC1,
         pca_genes_old[pca_genes_old$ID %in% pca_genes_new$ID,]$PC1)<0){
    pca_genes_new$PC1 = -pca_genes_new$PC1
  }
  if(cor(pca_genes_new[pca_genes_new$ID %in% pca_genes_old$ID,]$PC2, 
         pca_genes_old[pca_genes_old$ID %in% pca_genes_new$ID,]$PC2 )<0){
    pca_genes_new$PC2 = -pca_genes_new$PC2
  }
  
  pca_samps_old = pca_samps_new
  pca_genes_old = pca_genes_new
  
  # Update DFs
  pcas_samps = rbind(pcas_samps, pca_samps_new)
  pcas_genes = rbind(pcas_genes, pca_genes_new)
  
}

# Add Diagnosis/SFARI score information
pcas_samps = pcas_samps %>% mutate('ID'=substring(ID,2)) %>% 
             left_join(datMeta, by=c('ID'='Dissected_Sample_ID')) %>%
             dplyr::select(ID, PC1, PC2, fc, Diagnosis, Brain_lobe)
# pcas_genes = pcas_genes %>% left_join(SFARI_genes, by='ID') %>% 
#              mutate('score'=as.factor(`gene-score`)) %>%
#              dplyr::select(ID, PC1, PC2, lfc, score)

# Plot change of number of genes
ggplotly(data.frame('lfc'=log2(fc_list), 'n_genes'=n_genes[-1]) %>% ggplot(aes(x=lfc, y=n_genes)) + 
         geom_point() + geom_line() + theme_minimal() + xlab('|Log Fold Change|') + ylab('Remaining Genes') +
         ggtitle('Remaining genes when modifying filtering threshold'))

rm(fc_list, n_genes, fc, pca_samps_new, pca_genes_new, pca_samps_old, pca_genes_old, 
   datExpr_pca_samps, datExpr_pca_genes)

Samples

Note: PC values get smaller as Log2 fold change increases, so on each iteration the values were scaled so it would be easier to compare between frames

Coloured by Diagnosis:

• LFC = -1 represents the whole set of genes, without any filtering by differential expression

• Filtering out genes that are not differentially expressed (\(H_0:lfc=0\)) separates the two diagnosis groups a bit better than using all the genes

• Increasing the LFC threshold doesn’t seem to improve the separation between groups

ggplotly(pcas_samps %>% mutate(abs_lfc=ifelse(fc==-1,-1,round(log2(abs(fc)),3))) %>% 
         ggplot(aes(PC1, PC2, color=Diagnosis)) + geom_point(aes(frame=abs_lfc, ids=ID)) + 
         theme_minimal() + ggtitle('Samples PCA plot modifying filtering threshold'))

Genes

if(!'fcSign' %in% colnames(pcas_genes)){
  pcas_genes = pcas_genes %>% left_join(DE_info, by='ID') %>% mutate(fcSign = ifelse(log2FoldChange>0,'Positive','Negative')) 
}

ggplotly(pcas_genes %>% mutate(abs_lfc=ifelse(fc==-1,-1,round(log2(fc),3))) %>% 
         ggplot(aes(PC1, PC2, color=fcSign)) + geom_point(aes(frame=abs_lfc, ids=ID, alpha=0.1)) + 
         theme_minimal() + ggtitle('Genes PCA plot modifying |LFC| filtering threshold'))

---

Session info

sessionInfo()

## R version 3.6.0 (2019-04-26)
## Platform: x86_64-redhat-linux-gnu (64-bit)
## Running under: Scientific Linux 7.6 (Nitrogen)
## 
## Matrix products: default
## BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so
## 
## locale:
##  [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
##  [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
##  [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] knitr_1.24                  biomaRt_2.42.0             
##  [3] DESeq2_1.26.0               SummarizedExperiment_1.16.1
##  [5] DelayedArray_0.12.2         BiocParallel_1.20.1        
##  [7] matrixStats_0.55.0          Biobase_2.46.0             
##  [9] GenomicRanges_1.38.0        GenomeInfoDb_1.22.0        
## [11] IRanges_2.20.2              S4Vectors_0.24.3           
## [13] BiocGenerics_0.32.0         ClusterR_1.2.1             
## [15] gtools_3.8.1                Rtsne_0.15                 
## [17] GGally_1.4.0                gridExtra_2.3              
## [19] viridis_0.5.1               viridisLite_0.3.0          
## [21] RColorBrewer_1.1-2          plotlyutils_0.0.0.9000     
## [23] plotly_4.9.2                glue_1.3.1                 
## [25] reshape2_1.4.3              forcats_0.4.0              
## [27] stringr_1.4.0               dplyr_0.8.3                
## [29] purrr_0.3.3                 readr_1.3.1                
## [31] tidyr_1.0.2                 tibble_2.1.3               
## [33] ggplot2_3.2.1               tidyverse_1.3.0            
## 
## loaded via a namespace (and not attached):
##   [1] readxl_1.3.1           backports_1.1.5        Hmisc_4.2-0           
##   [4] BiocFileCache_1.10.2   plyr_1.8.5             lazyeval_0.2.2        
##   [7] splines_3.6.0          gmp_0.5-13.6           crosstalk_1.0.0       
##  [10] digest_0.6.24          htmltools_0.4.0        fansi_0.4.1           
##  [13] magrittr_1.5           checkmate_1.9.4        memoise_1.1.0         
##  [16] cluster_2.0.8          annotate_1.64.0        modelr_0.1.5          
##  [19] askpass_1.1            prettyunits_1.0.2      colorspace_1.4-1      
##  [22] blob_1.2.1             rvest_0.3.5            rappdirs_0.3.1        
##  [25] haven_2.2.0            xfun_0.8               crayon_1.3.4          
##  [28] RCurl_1.95-4.12        jsonlite_1.6           genefilter_1.68.0     
##  [31] survival_2.44-1.1      gtable_0.3.0           zlibbioc_1.32.0       
##  [34] XVector_0.26.0         scales_1.1.0           DBI_1.1.0             
##  [37] miniUI_0.1.1.1         Rcpp_1.0.3             xtable_1.8-4          
##  [40] progress_1.2.2         htmlTable_1.13.1       foreign_0.8-71        
##  [43] bit_1.1-15.2           Formula_1.2-3          htmlwidgets_1.5.1     
##  [46] httr_1.4.1             acepack_1.4.1          farver_2.0.3          
##  [49] pkgconfig_2.0.3        reshape_0.8.8          XML_3.99-0.3          
##  [52] nnet_7.3-12            dbplyr_1.4.2           locfit_1.5-9.1        
##  [55] tidyselect_0.2.5       labeling_0.3           rlang_0.4.4           
##  [58] later_1.0.0            AnnotationDbi_1.48.0   munsell_0.5.0         
##  [61] cellranger_1.1.0       tools_3.6.0            cli_2.0.1             
##  [64] generics_0.0.2         RSQLite_2.2.0          broom_0.5.4           
##  [67] fastmap_1.0.1          evaluate_0.14          yaml_2.2.0            
##  [70] bit64_0.9-7            fs_1.3.1               nlme_3.1-139          
##  [73] mime_0.9               ggExtra_0.9            xml2_1.2.2            
##  [76] compiler_3.6.0         rstudioapi_0.10        curl_4.3              
##  [79] reprex_0.3.0           geneplotter_1.64.0     stringi_1.4.6         
##  [82] highr_0.8              lattice_0.20-38        Matrix_1.2-17         
##  [85] vctrs_0.2.2            pillar_1.4.3           lifecycle_0.1.0       
##  [88] data.table_1.12.8      bitops_1.0-6           httpuv_1.5.2          
##  [91] R6_2.4.1               latticeExtra_0.6-28    promises_1.1.0        
##  [94] assertthat_0.2.1       openssl_1.4.1          withr_2.1.2           
##  [97] GenomeInfoDbData_1.2.2 hms_0.5.3              grid_3.6.0            
## [100] rpart_4.1-15           rmarkdown_1.14         Cairo_1.5-10          
## [103] shiny_1.4.0            lubridate_1.7.4        base64enc_0.1-3